Incrementality testing has become a cornerstone of marketing measurement for many brands. It’s easy to understand why—the promise of measuring true lift from your marketing efforts is compelling. But there’s a dangerous misconception spreading through marketing departments and agencies: the idea that all incrementality tests are equivalent to rigorous randomized controlled trials (RCTs). This comparison lends these tests an air of pure science they haven’t earned, and the resulting false confidence can lead to costly mistakes in marketing strategy.
Understanding true randomized controlled trials (RCTs)
The term “RCT” covers a wide spectrum of experimental approaches, each with different levels of control and rigor. At their most basic, RCTs involve random assignment of subjects to treatment and control groups. But how these groups are handled creates meaningful differences in the quality and reliability of results.
Single-blind trials, where either the subjects or the researchers (but not both) know who’s receiving treatment, represent one common type of RCT. These offer more rigor than unblinded studies but still leave room for bias. Moving up the quality scale, double-blind trials remove more potential for bias by keeping both subjects and researchers in the dark about group assignments. Some studies go even further with triple-blind protocols, where even the data analysts don’t know which group is which until after their analysis.
But even within these categories, the quality of RCTs can vary dramatically based on factors like:
- How thoroughly potential confounding variables are controlled
- The size and representativeness of the sample
- The precision of the randomization process
- The completeness of the blinding protocols
- The duration of the study relative to the effect being measured
By these standards, incrementality tests are a type of RCT, but they’re definitely not the most rigorous type. Before we dive into why incrementality tests fall lower on the quality scale, let’s establish what makes the most rigorous RCTs the gold standard in scientific research.
What makes the most rigorous RCTs different
When researchers talk about RCTs as the pinnacle of experimental design, they’re referring to the most rigorous implementation possible. Think of these as the Formula 1 cars of the research world—precision-engineered experiments where every detail is carefully controlled.
These highest-quality RCTs require:
- Genuine randomization in subject selection and group assignment
- Strictly isolated control groups with no possibility of cross-contamination
- Double-blind protocols (at minimum) to prevent both conscious and unconscious bias
- Careful control of all variables that could influence results
- Predetermined analysis plans to prevent post-hoc rationalization
- Sufficient sample sizes to achieve statistical significance
- Clear criteria for subject inclusion and exclusion
- Comprehensive documentation of all procedures and protocols
These requirements aren’t just bureaucratic check boxes—they’re essential safeguards that ensure the results actually mean what we think they mean. Without them, we’re just looking at correlation, not causation.
The reality of incrementality tests
Marketing teams often turn to incrementality testing hoping to understand the true impact of their campaigns. While these tests are RCTs because they rely on some of the same principles, they face fundamental limitations that make truly rigorous experimental conditions near-impossible.
The structure seems simple enough: create two groups, show ads to one but not the other, and measure the difference in outcomes. (We’re ignoring synthetic controls for a moment and focusing on the test design.) But this oversimplification masks critical flaws that prevent incrementality tests from achieving the same level as the highest quality RCTs.
Critical differences between incrementality tests and rigorous RCTs
When we break down the specific ways incrementality tests fall short, three major categories emerge. Each highlights how these tests, despite their best attempts at scientific rigor, simply haven’t replicated the controlled conditions that make the highest quality RCTs reliable. (Theoretically, someone could create a test that is reliable, so we’re not saying it can’t be done but that it hasn’t been.) Understanding these differences isn’t just academic—it’s crucial for marketers who need to know exactly how much faith to put in their test results.
Population control
Let’s be honest: matching demographic groups across different regions is about as precise as throwing darts blindfolded. New York and San Francisco might look similar on paper, but their consumers behave drastically differently. Even more problematic? You can’t stop people from moving between your test and control groups. Your carefully selected audiences aren’t as isolated as you think.
External events
Think you can control for market conditions across different regions? Think again. Your test group might be experiencing a local economic boom while your control group faces a downturn. Your competitor might launch a major campaign in one region but not another. These external variables can dwarf the effects you’re trying to measure.
Temporal limitations
Marketing isn’t a snapshot. It’s a movie. Incrementality tests give you a single frame, violating the fundamental truth that marketing effects unfold over time. Today’s awareness campaign might not drive conversions for weeks or months. How do you capture that in a time-boxed test?
The dangers of incorrect context
Here’s where things get really problematic. Believing incrementality tests are as reliable as the highest quality RCTs isn’t just an academic concern—it has real, costly implications for marketing organizations. Let’s break down the specific dangers this misconception creates.
Risk of over-reliance on flawed data
When marketers believe incrementality tests have the highest quality RCT-level rigor without verifying that belief, they tend to weigh these results too heavily in their decision-making process. This over-reliance can lead to overconfidence in the data’s reliability. Think about it: if you believe you’re working with gold-standard scientific data, you’re more likely to make bold, definitive moves based on that data. But if the foundation is shaky, those moves could be taking you in exactly the wrong direction.
Impact on MMM training and outputs
This is where the damage can really compound. Many marketers use incrementality test results to train their marketing mix models, thinking they’re feeding their models pristine scientific data. But here’s the reality: if you train an MMM on flawed incrementality data, you’re not just making one mistake—you’re building that mistake into every future prediction your model makes. It’s like using a crooked ruler to measure everything; each measurement will be off, and you’ll never know unless you check against a true straight edge. (This is one of the reasons we built Validation Layer. It’s not that incrementality tests always make your MMM worse, but you should be able to see whether it hurts or benefits your accuracy before integrating it.)
Potential for misguided marketing decisions
The cascade effect continues into your strategic decisions. When marketers believe they have rigorous data showing certain channels or campaigns are incrementally effective (or ineffective), they make major budget allocation decisions based on these findings. These decisions might involve:
- Significantly scaling spend in channels that showed positive incrementality
- Cutting or eliminating channels that didn’t show strong incremental results
- Restructuring entire marketing strategies around flawed incrementality insights
- Making long-term commitment decisions about marketing platforms or partners
Cost implications of false confidence
Finally, there’s the bottom-line impact. The financial implications of this incorrect context can be severe:
- Direct costs of running expensive incrementality tests that don’t deliver the scientific rigor you think you’re paying for
- Opportunity costs from misallocating budget based on flawed incrementality data
- Wasted spend from scaling campaigns that aren’t actually performing as well as your incrementality tests suggest
- Long-term revenue loss from cutting truly effective channels that your incrementality tests failed to properly measure
This is precisely why validation is so crucial. Before you make major strategic decisions based on incrementality test results, you need to understand exactly how reliable those results are. You need to know whether incorporating this data into your measurement framework is helping or hurting your ability to make accurate marketing decisions.
Better approaches to marketing measurement
The solution isn’t to abandon incrementality testing entirely, it’s to understand its proper role in your measurement stack and validate its results. This is where Prescient’s approach stands apart: our platform can run your marketing mix model both with and without your incrementality test data, comparing accuracy to determine whether including this data helps or hurts your model’s performance.
This validation step isn’t just nice to have—it’s crucial. Running an MMM without checking whether your incrementality data improves or degrades its accuracy is like building a house without checking the foundation. You might get lucky, but why take that risk with your marketing budget?
Consider these approaches for more reliable measurement:
- Use multiple measurement methodologies to cross-validate findings
- Validate incrementality data before incorporating it into your MMM
- Focus on long-term trends rather than point-in-time measurements
- Consider the full context of your marketing environment
Wrapping it up…
Incrementality tests can be valuable tools when used appropriately, but treating them as equivalent to the most rigorous RCTs is dangerous wishful thinking. The key is understanding their limitations and validating their results against other measurement approaches.
Recommendations for marketers
Before your next incrementality test:
- Question provider claims about “experimental conditions”
- Consider what external factors might influence your results
- Plan for validation through tools like Prescient’s MMM
- Build a measurement framework that doesn’t rely too heavily on any single methodology
Remember, acknowledging the limitations of our measurement tools isn’t a weakness. Doing so is the first step toward more accurate and effective marketing decisions. If you’re ready to validate your incrementality tests and build a more reliable measurement framework, we’d love to show you how Prescient can help.
